abstract

Deep networks for predicting human intent with respect to objects

Authors:

Mircea NicolescuAuthors Info & Claims

HRI '12: Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction

Pages 171 - 172

https://doi.org/10.1145/2157689.2157740

Published: 05 March 2012 Publication History

Get Access

Abstract

Effective human-robot interaction requires systems that can accurately infer and predict human intentions. In this paper, we introduce a system that uses stacked denoising autoencoders to perform intent recognition. We introduce the intent recognition problem, provide an overview of deep architectures in machine learning, and outline the components of our system. We also provide preliminary results for our system's performance.

References

[1]

Y. Demiris. Prediction of intent in robotics and multi-agent systems. Cognitive Processing, 8(3):151--158, September 2007.

Crossref

Google Scholar

[2]

R. Kelley, A. Ambardekar, L. Wigand, M. Nicolescu, and M. Nicolescu. Point clouds and rnage images for intent recognition and human-robot interaction. In RGB-D: Advanced Reasoning with Depth Cameras, June 2011.

Google Scholar

[3]

R. Kelley, A. Tavakkoli, C. King, M. Nicolescu, M. Nicolescu, and G. Bebis. Understanding human intentions via hidden markov models in autonomous mobile robots. In ACM/IEEE International Conference on Human Robot Interaction, February 2010.

Digital Library

Google Scholar

[4]

J. Y. Sung, C. Ponce, B. Selman, and A. Saxena. Human activity detection from rgbd images. In Plan, Activity and Intent Recognition, August 2011.

Google Scholar

[5]

G. Taylor, G. Hinton, and S. Roweis. Modeling human motion using binary latent variables. Neural Information Processing Systems, 19:1345--1352, January 2007.

Google Scholar

[6]

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11:3371--3408, December 2010.

Digital Library

Google Scholar

Cited By

View all

Lopez-de-Ipina KIradi JFernandez ECalvo PSalle DPoologaindran AVillaverde IDaelman PSanchez ERequejo CSuckling J(2023)HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative RoboticsSensors10.3390/s2303117023:3(1170)Online publication date: 19-Jan-2023
https://doi.org/10.3390/s23031170
Alsamhi SMa OAnsari M(2022)Survey on artificial intelligence based techniques for emerging robotic communicationTelecommunications Systems10.1007/s11235-019-00561-z72:3(483-503)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.1007/s11235-019-00561-z
Persiani MHellström T(2020)Intent Recognition from Speech and Plan RecognitionAdvances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection10.1007/978-3-030-49778-1_17(212-223)Online publication date: 15-Jun-2020
https://doi.org/10.1007/978-3-030-49778-1_17
Show More Cited By

Index Terms

Deep networks for predicting human intent with respect to objects
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. External interfaces for robotics
    2. Sensors and actuators

Recommendations

Lexical Entrainment in Multi-party Human–Robot Interaction
Social Robotics
Abstract
This paper reports lexical entrainment in a multi-party human–robot interaction, wherein one robot and two humans serve as participants. Humans tend to use the same terms as their interlocutors while making conversation. This phenomenon is called ...
Human-robot collaborative tutoring using multiparty multimodal spoken dialogue
HRI '14: Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction

In this paper, we describe a project that explores a novel experimental setup towards building a spoken, multi-modally rich, and human-like multiparty tutoring robot. A human-robot interaction setup is designed, and a human-human dialogue corpus is ...
The effect of head-nod recognition in human-robot conversation
HRI '06: Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction

This paper reports on a study of human participants with a robot designed to participate in a collaborative conversation with a human. The purpose of the study was to investigate a particular kind of gestural feedback from human to the robot in these ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

HRI '12: Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction

March 2012

518 pages

ISBN:9781450310635

DOI:10.1145/2157689

General Chairs:
Holly Yanco
University of Massachusetts Lowell, USA
,
Aaron Steinfeld
Carnegie Mellon University, USA
,
Program Chairs:
Vanessa Evers
University of Amsterdam, The Netherlands
,
Odest Chadwicke Jenkins
Brown University, USA

In-Cooperation

IEEE-RAS: Robotics and Automation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 March 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Abstract

Conference

HRI'12

Sponsor:

HRI'12: International Conference on Human-Robot Interaction

March 5 - 8, 2012

Massachusetts, Boston, USA

Acceptance Rates

Overall Acceptance Rate 268 of 1,124 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
450
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Lopez-de-Ipina KIradi JFernandez ECalvo PSalle DPoologaindran AVillaverde IDaelman PSanchez ERequejo CSuckling J(2023)HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative RoboticsSensors10.3390/s2303117023:3(1170)Online publication date: 19-Jan-2023
https://doi.org/10.3390/s23031170
Alsamhi SMa OAnsari M(2022)Survey on artificial intelligence based techniques for emerging robotic communicationTelecommunications Systems10.1007/s11235-019-00561-z72:3(483-503)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.1007/s11235-019-00561-z
Persiani MHellström T(2020)Intent Recognition from Speech and Plan RecognitionAdvances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection10.1007/978-3-030-49778-1_17(212-223)Online publication date: 15-Jun-2020
https://doi.org/10.1007/978-3-030-49778-1_17
Koch JOulasvirta A(2018)Group Cognition and Collaborative AIHuman and Machine Learning10.1007/978-3-319-90403-0_15(293-312)Online publication date: 8-Jun-2018
https://doi.org/10.1007/978-3-319-90403-0_15
Wayllace CHou PYeoh W(2017)New metrics and algorithms for stochastic goal recognition design problemsProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171837.3171909(4455-4462)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3171837.3171909
Zhang LDiao XMa O(2017)A Preliminary Study on a Robot's Prediction of Human Intention2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)10.1109/CYBER.2017.8446086(1446-1450)Online publication date: Jul-2017
https://doi.org/10.1109/CYBER.2017.8446086
Liu WWang ZLiu XZeng NLiu YAlsaadi F(2017)A survey of deep neural network architectures and their applicationsNeurocomputing10.1016/j.neucom.2016.12.038234(11-26)Online publication date: Apr-2017
https://doi.org/10.1016/j.neucom.2016.12.038
Ang SChan HJiang AYeoh W(2017)Game-Theoretic Goal Recognition Models with Applications to Security DomainsDecision and Game Theory for Security10.1007/978-3-319-68711-7_14(256-272)Online publication date: 4-Oct-2017
https://doi.org/10.1007/978-3-319-68711-7_14
Wayllace CHou PYeoh WSon T(2016)Goal recognition design with stochastic agent action outcomesProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3061053.3061080(3279-3285)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3061053.3061080
Han JChoi SKim J(2016)Interactive Human Intention Reading by Learning Hierarchical Behavior Knowledge Networks for Human-Robot InteractionETRI Journal10.4218/etrij.16.0116.010638:6(1229-1239)Online publication date: 1-Dec-2016
https://doi.org/10.4218/etrij.16.0116.0106
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Lexical Entrainment in Multi-party Human–Robot Interaction

Human-robot collaborative tutoring using multiparty multimodal spoken dialogue

The effect of head-nod recognition in human-robot conversation